Devising Asymmetric Linguistic Hedges to Enhance the Accuracy of NEFCLASS for Datasets with Highly Skewed Feature Values

Jamileh Yousefi

Abstract

This paper presents a model to address the skewness problem in the NEFCLASS classifier by devising several novel asymmetric linguistic hedges within the classifier. NEFCLASS is a common example of the construction of a NEURO-FUZZY system. The NEFCLASS performs increasingly poorly as data skewness increases. This poses a challenge for the classification of biological data that commonly exhibits feature value skewness. The objective of this paper is to device several novel asymmetric linguistic hedges to modify the shape of membership functions, hence improving the accuracy of NEFCLASS. This study demonstrated that devising an appropriate asymmetric linguistic hedge significantly improves the accuracy of NEFCLASS for skewed data.

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